{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas需要先读取表格类型的数据，然后进行分析\n",
    "\n",
    "| 数据类型 | 说明 | Pandas读取方法 |\n",
    "| :-----:| :----: | :----: |\n",
    "| csv、tsv、txt | 用逗号分隔开、tab分割的纯文本文件 | pd.read_csv |\n",
    "| excel | 微软xls或者xlsx | pd.read_excel |\n",
    "| mysql | 关系型数据表 | pd.read_sql |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取纯文本文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取csv，使用默认的标题行、逗号分割符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviePath=\"./datas/movie.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv(moviePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1        1     4.0\n",
       "1       1        3     5.0\n",
       "2       1        4     4.0\n",
       "3       1        2     5.0\n",
       "4       1        6     3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示前5个\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的形状，返回（行数、列数）\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'rating'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看列名列表\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=15, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看索引列\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       int64\n",
       "movieId      int64\n",
       "rating     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看每列的数据类型\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取txt,自己指定分割符、列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "txtPath = \"./datas/log.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdata = pd.read_csv(txtPath,sep='*',header=None,names=['data','pv','uv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>200</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>2320</td>\n",
       "      <td>3332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>2030</td>\n",
       "      <td>3311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>2200</td>\n",
       "      <td>3322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>1200</td>\n",
       "      <td>3111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>2030</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data    pv    uv\n",
       "0  2020-10-21   200  4331\n",
       "1  2020-10-21  2320  3332\n",
       "2  2020-10-21  2030  3311\n",
       "3  2020-10-21  2200  3322\n",
       "4  2020-10-21  1200  3111\n",
       "5  2020-10-21  2030   323"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取excel文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "excelPath = \"./datas/hotel.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "exdata = pd.read_excel(excelPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>省份</th>\n",
       "      <th>城市</th>\n",
       "      <th>酒店名称</th>\n",
       "      <th>婚宴价格</th>\n",
       "      <th>地址</th>\n",
       "      <th>容纳桌数</th>\n",
       "      <th>酒店详情地址</th>\n",
       "      <th>酒店标签</th>\n",
       "      <th>酒店标签.1</th>\n",
       "      <th>详细地址</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1583656551-3414</td>\n",
       "      <td>https://sz.tuanyanwang.com/hunyan-page5</td>\n",
       "      <td>江苏</td>\n",
       "      <td>苏州</td>\n",
       "      <td>唐宫美罗海鲜舫</td>\n",
       "      <td>￥3488-5988/桌</td>\n",
       "      <td>观前街</td>\n",
       "      <td>容纳16桌</td>\n",
       "      <td>https://sz.tuanyanwang.com/shop/4246</td>\n",
       "      <td>中式婚礼</td>\n",
       "      <td>中式婚礼</td>\n",
       "      <td>观前街</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1583666978-4551</td>\n",
       "      <td>https://shenzhen.tuanyanwang.com/hunyan-page7</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>佳宁娜贵宾楼</td>\n",
       "      <td>￥2880-10888/桌</td>\n",
       "      <td>福田区 福华新村</td>\n",
       "      <td>容纳60桌</td>\n",
       "      <td>https://shenzhen.tuanyanwang.com/shop/3813</td>\n",
       "      <td>中式婚礼西式婚礼</td>\n",
       "      <td>中式婚礼西式婚礼</td>\n",
       "      <td>福华新村</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1583652550-2201</td>\n",
       "      <td>https://hz.tuanyanwang.com/hunyan-page13</td>\n",
       "      <td>浙江</td>\n",
       "      <td>杭州</td>\n",
       "      <td>五洋宾馆</td>\n",
       "      <td>￥2380-3880/桌</td>\n",
       "      <td>下城区 潮鸣</td>\n",
       "      <td>容纳25桌</td>\n",
       "      <td>https://hz.tuanyanwang.com/shop/2984</td>\n",
       "      <td>中式婚礼西式婚礼</td>\n",
       "      <td>中式婚礼西式婚礼</td>\n",
       "      <td>下城区庆春路</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                          web-scraper-start-url  省份  城市  \\\n",
       "0   1583656551-3414        https://sz.tuanyanwang.com/hunyan-page5  江苏  苏州   \n",
       "1   1583666978-4551  https://shenzhen.tuanyanwang.com/hunyan-page7  广东  深圳   \n",
       "2   1583652550-2201       https://hz.tuanyanwang.com/hunyan-page13  浙江  杭州   \n",
       "\n",
       "      酒店名称           婚宴价格        地址   容纳桌数  \\\n",
       "0  唐宫美罗海鲜舫   ￥3488-5988/桌       观前街  容纳16桌   \n",
       "1   佳宁娜贵宾楼  ￥2880-10888/桌  福田区 福华新村  容纳60桌   \n",
       "2     五洋宾馆   ￥2380-3880/桌    下城区 潮鸣  容纳25桌   \n",
       "\n",
       "                                       酒店详情地址      酒店标签    酒店标签.1    详细地址  \n",
       "0        https://sz.tuanyanwang.com/shop/4246      中式婚礼      中式婚礼     观前街  \n",
       "1  https://shenzhen.tuanyanwang.com/shop/3813  中式婚礼西式婚礼  中式婚礼西式婚礼    福华新村  \n",
       "2        https://hz.tuanyanwang.com/shop/2984  中式婚礼西式婚礼  中式婚礼西式婚礼  下城区庆春路  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exdata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取Mysql数据库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql\n",
    "conn = pymysql.connect(\n",
    "    host = '127.0.0.1',\n",
    "    user = 'root',\n",
    "    password = '123456',\n",
    "    database='rbac',\n",
    "    charset = 'utf8'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mysql_data = pd.read_sql(\"select * from role\",con=conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>roleId</th>\n",
       "      <th>describe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>admin</td>\n",
       "      <td>超级管理员</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>editor</td>\n",
       "      <td>编辑者</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>operate</td>\n",
       "      <td>操作员</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   roleId describe\n",
       "0   1    admin    超级管理员\n",
       "1   2   editor      编辑者\n",
       "2   3  operate      操作员"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mysql_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas数据结构（DataFrame && Series）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 介绍"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series\n",
    "Series是一种类似于一维数组的对象，它由一组数据（不同数据类型）以及一组与之相关的数据标签（即索引）组成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 仅有数据列表即可产生最简单的Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series(['a',1,2.3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      a\n",
       "1      1\n",
       "2    2.3\n",
       "3      5\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 左侧为索引，右侧为数据\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取索引\n",
    "s1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 1, 2.3, 5], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数据\n",
    "s1.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建一个具有标签索引的Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series(['a',1,2.3,5],index=['d','a','c','b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d      a\n",
       "a      1\n",
       "c    2.3\n",
       "b      5\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d', 'a', 'c', 'b'], dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用python字典创建Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicts = {'name':'taisoulx','age':18,'sex':'m'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = pd.Series(dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name    taisoulx\n",
       "age           18\n",
       "sex            m\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据索引标签查询数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d      a\n",
       "a      1\n",
       "c    2.3\n",
       "b      5\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s2['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    5\n",
       "a    1\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[['b','a']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "DataFrame是一个表格型的数据结构\n",
    "* 每列可以是不同的值类型（数值、字符串、布尔值等）\n",
    "* 既有行索引index也有列索引columns\n",
    "* 可以被看作由Series组成的字典"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataF = {\n",
    "    'state':['greate','nice','normal','bad','greate'],\n",
    "    'year':[2000,2001,2002,2003,2004],\n",
    "    'pop':[1.2,1.3,2.1,3.1,4.4]\n",
    "}\n",
    "df =pd.DataFrame(dataF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>greate</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nice</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>normal</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bad</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>greate</td>\n",
       "      <td>2004</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0  greate  2000  1.2\n",
       "1    nice  2001  1.3\n",
       "2  normal  2002  2.1\n",
       "3     bad  2003  3.1\n",
       "4  greate  2004  4.4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state     object\n",
       "year       int64\n",
       "pop      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 值类型\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'year', 'pop'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000\n",
       "1    2001\n",
       "2    2002\n",
       "3    2003\n",
       "4    2004\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询一列，返回一个Series\n",
    "df['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>greate</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nice</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>normal</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bad</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>greate</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year\n",
       "0  greate  2000\n",
       "1    nice  2001\n",
       "2  normal  2002\n",
       "3     bad  2003\n",
       "4  greate  2004"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询大于1列，返回一个DataFrame\n",
    "df[['state','year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state    nice\n",
       "year     2001\n",
       "pop       1.3\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询1行，返回一个Series\n",
    "df.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nice</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>normal</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bad</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "1    nice  2001  1.3\n",
       "2  normal  2002  2.1\n",
       "3     bad  2003  3.1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询多行，返回一个DataFrame,包含第三个元素\n",
    "df.loc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas数据查询\n",
    "按**数值、列表、区间、条件、函数**五种方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas查询数据的几种方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1.df.loc方法-->根据行、列的标签值查询\n",
    "* 2.df.iloc方法-->根据行、列的数字位置查询\n",
    "* 3.df.where方法\n",
    "* 4.df.query方法\n",
    "\n",
    "**.loc既能查询，又能覆盖写入**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas 使用df.loc查询数据的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1.使用单个lable值查询数据\n",
    "* 2.使用值列表批量查询\n",
    "* 3.使用数值区间进行范围查询\n",
    "* 4.使用条件表达式查询\n",
    "* 5.调用函数查询\n",
    "\n",
    "**注意**\n",
    "* 以上方法，既适用于行，也适用于列\n",
    "* 注意观察降维DataFrame>Series>值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "wPath = \"./datas/SURF_CHN_MUL_STATION.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "wData = pd.read_excel(wPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>省份</th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "      <th>开始年份</th>\n",
       "      <th>结束年份</th>\n",
       "      <th>缺测时段</th>\n",
       "      <th>标准值统计时段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50136</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>漠河</td>\n",
       "      <td>122.31</td>\n",
       "      <td>52.58</td>\n",
       "      <td>433.0</td>\n",
       "      <td>434.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-199612/199701-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50137</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>北极村</td>\n",
       "      <td>122.22</td>\n",
       "      <td>53.28</td>\n",
       "      <td>296.0</td>\n",
       "      <td>297.3</td>\n",
       "      <td>200201</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/////</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50246</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>塔河</td>\n",
       "      <td>124.43</td>\n",
       "      <td>52.21</td>\n",
       "      <td>361.9</td>\n",
       "      <td>363.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50247</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>呼中</td>\n",
       "      <td>123.34</td>\n",
       "      <td>52.02</td>\n",
       "      <td>514.5</td>\n",
       "      <td>514.9</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50349</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>新林</td>\n",
       "      <td>124.24</td>\n",
       "      <td>51.40</td>\n",
       "      <td>501.5</td>\n",
       "      <td>502.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50353</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>呼玛</td>\n",
       "      <td>126.38</td>\n",
       "      <td>51.44</td>\n",
       "      <td>177.4</td>\n",
       "      <td>178.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50425</td>\n",
       "      <td>内蒙古</td>\n",
       "      <td>额尔古纳市</td>\n",
       "      <td>120.11</td>\n",
       "      <td>50.15</td>\n",
       "      <td>581.4</td>\n",
       "      <td>582.3</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50431</td>\n",
       "      <td>内蒙古</td>\n",
       "      <td>根河市</td>\n",
       "      <td>121.31</td>\n",
       "      <td>50.47</td>\n",
       "      <td>717.0</td>\n",
       "      <td>718.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-200412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     区站号   省份     站名      经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)    开始年份    结束年份  \\\n",
       "0  50136  黑龙江     漠河  122.31  52.58       433.0         434.2  198101  201012   \n",
       "1  50137  黑龙江    北极村  122.22  53.28       296.0         297.3  200201  201012   \n",
       "2  50246  黑龙江     塔河  124.43  52.21       361.9         363.0  198101  201012   \n",
       "3  50247  黑龙江     呼中  123.34  52.02       514.5         514.9  198101  201012   \n",
       "4  50349  黑龙江     新林  124.24  51.40       501.5         502.2  198101  201012   \n",
       "5  50353  黑龙江     呼玛  126.38  51.44       177.4         178.0  198101  201012   \n",
       "6  50425  内蒙古  额尔古纳市  120.11  50.15       581.4         582.3  198101  201012   \n",
       "7  50431  内蒙古    根河市  121.31  50.47       717.0         718.2  198101  201012   \n",
       "\n",
       "  缺测时段                      标准值统计时段  \n",
       "0  NaN  198101-199612/199701-201012  \n",
       "1  NaN                        /////  \n",
       "2  NaN                198101-201012  \n",
       "3  NaN                198101-201012  \n",
       "4  NaN                198101-201012  \n",
       "5  NaN                198101-201012  \n",
       "6  NaN                198101-201012  \n",
       "7  NaN                198101-200412  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wData.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=2160, step=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wData.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['区站号', '省份', '站名', '经度', '纬度', '观测场拔海高度（米）', '气压传感器拔海高度(米)', '开始年份',\n",
       "       '结束年份', '缺测时段', '标准值统计时段'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wData.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用单个lable值查询数据\n",
    "行或者列，都可以只传入单个值，实现精确匹配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "751.5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到单个值\n",
    "wData.loc[500,'气压传感器拔海高度(米)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "站名                         尉氏\n",
       "气压传感器拔海高度(米)             68.5\n",
       "标准值统计时段         198101-201012\n",
       "Name: 1212, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个Series\n",
    "wData.loc[1212,['站名','气压传感器拔海高度(米)','标准值统计时段']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用值列表批量查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      178.0\n",
       "100    282.0\n",
       "300    910.5\n",
       "Name: 气压传感器拔海高度(米), dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到Series\n",
    "wData.loc[[5,100,300],'气压传感器拔海高度(米)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361.9</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>1185.1</td>\n",
       "      <td>1186.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>818.2</td>\n",
       "      <td>819.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      观测场拔海高度（米）  气压传感器拔海高度(米)\n",
       "2          361.9         363.0\n",
       "322       1185.1        1186.1\n",
       "1231       818.2         819.4"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到DataFrame\n",
    "wData.loc[[2,322,1231],['观测场拔海高度（米）','气压传感器拔海高度(米)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用数值区间进行范围查询\n",
    "注意：区间既包含开始，也包含结束（和python本身的特点有所区别）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    297.3\n",
       "2    363.0\n",
       "3    514.9\n",
       "4    502.2\n",
       "5    178.0\n",
       "6    582.3\n",
       "Name: 气压传感器拔海高度(米), dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行index按区间\n",
    "wData.loc[1:6,'气压传感器拔海高度(米)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "站名                  泽库\n",
       "经度              101.28\n",
       "纬度               35.02\n",
       "观测场拔海高度（米）      3662.8\n",
       "气压传感器拔海高度(米)    3663.6\n",
       "Name: 210, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列index按区间\n",
    "wData.loc[210,'站名':'气压传感器拔海高度(米)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>皋兰</td>\n",
       "      <td>103.57</td>\n",
       "      <td>36.21</td>\n",
       "      <td>1668.5</td>\n",
       "      <td>1669.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>永登</td>\n",
       "      <td>103.15</td>\n",
       "      <td>36.45</td>\n",
       "      <td>2118.8</td>\n",
       "      <td>2120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>靖远</td>\n",
       "      <td>104.41</td>\n",
       "      <td>36.34</td>\n",
       "      <td>1398.2</td>\n",
       "      <td>1399.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>白银</td>\n",
       "      <td>104.09</td>\n",
       "      <td>36.33</td>\n",
       "      <td>1738.7</td>\n",
       "      <td>1739.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>五道梁</td>\n",
       "      <td>93.05</td>\n",
       "      <td>35.13</td>\n",
       "      <td>4612.2</td>\n",
       "      <td>4613.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>沙珠玉</td>\n",
       "      <td>100.16</td>\n",
       "      <td>36.16</td>\n",
       "      <td>2872.0</td>\n",
       "      <td>2872.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>兴海</td>\n",
       "      <td>99.59</td>\n",
       "      <td>35.35</td>\n",
       "      <td>3323.2</td>\n",
       "      <td>3324.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      站名      经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)\n",
       "200   皋兰  103.57  36.21      1668.5        1669.7\n",
       "201   永登  103.15  36.45      2118.8        2120.0\n",
       "202   靖远  104.41  36.34      1398.2        1399.4\n",
       "203   白银  104.09  36.33      1738.7        1739.9\n",
       "204  五道梁   93.05  35.13      4612.2        4613.0\n",
       "205  沙珠玉  100.16  36.16      2872.0        2872.8\n",
       "206   兴海   99.59  35.35      3323.2        3324.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行列都按区间\n",
    "wData.loc[200:206,'站名':'气压传感器拔海高度(米)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用表达式进行查询\n",
    "bool列表的长度得等于行数或者列数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>省份</th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "      <th>开始年份</th>\n",
       "      <th>结束年份</th>\n",
       "      <th>缺测时段</th>\n",
       "      <th>标准值统计时段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>52908</td>\n",
       "      <td>青海</td>\n",
       "      <td>五道梁</td>\n",
       "      <td>93.05</td>\n",
       "      <td>35.13</td>\n",
       "      <td>4612.2</td>\n",
       "      <td>4613.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>55228</td>\n",
       "      <td>西藏</td>\n",
       "      <td>狮泉河</td>\n",
       "      <td>80.05</td>\n",
       "      <td>32.30</td>\n",
       "      <td>4278.6</td>\n",
       "      <td>4279.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>55248</td>\n",
       "      <td>西藏</td>\n",
       "      <td>改则</td>\n",
       "      <td>84.25</td>\n",
       "      <td>32.09</td>\n",
       "      <td>4414.9</td>\n",
       "      <td>4416.1</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>55299</td>\n",
       "      <td>西藏</td>\n",
       "      <td>那曲</td>\n",
       "      <td>92.04</td>\n",
       "      <td>31.29</td>\n",
       "      <td>4507.0</td>\n",
       "      <td>4508.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>55493</td>\n",
       "      <td>西藏</td>\n",
       "      <td>当雄</td>\n",
       "      <td>91.06</td>\n",
       "      <td>30.29</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4201.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>55664</td>\n",
       "      <td>西藏</td>\n",
       "      <td>定日</td>\n",
       "      <td>87.05</td>\n",
       "      <td>28.38</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>4301.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>55680</td>\n",
       "      <td>西藏</td>\n",
       "      <td>江孜</td>\n",
       "      <td>89.36</td>\n",
       "      <td>28.55</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>4041.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>56004</td>\n",
       "      <td>青海</td>\n",
       "      <td>沱沱河</td>\n",
       "      <td>92.26</td>\n",
       "      <td>34.13</td>\n",
       "      <td>4533.1</td>\n",
       "      <td>4533.9</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>56016</td>\n",
       "      <td>青海</td>\n",
       "      <td>治多</td>\n",
       "      <td>95.37</td>\n",
       "      <td>33.51</td>\n",
       "      <td>4179.1</td>\n",
       "      <td>4179.9</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>56018</td>\n",
       "      <td>青海</td>\n",
       "      <td>杂多</td>\n",
       "      <td>95.17</td>\n",
       "      <td>32.53</td>\n",
       "      <td>4066.4</td>\n",
       "      <td>4067.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>56021</td>\n",
       "      <td>青海</td>\n",
       "      <td>曲麻莱</td>\n",
       "      <td>95.48</td>\n",
       "      <td>34.07</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>4175.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>56033</td>\n",
       "      <td>青海</td>\n",
       "      <td>玛多</td>\n",
       "      <td>98.13</td>\n",
       "      <td>34.55</td>\n",
       "      <td>4272.3</td>\n",
       "      <td>4273.1</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>56034</td>\n",
       "      <td>青海</td>\n",
       "      <td>清水河</td>\n",
       "      <td>97.08</td>\n",
       "      <td>33.48</td>\n",
       "      <td>4415.4</td>\n",
       "      <td>4416.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>56038</td>\n",
       "      <td>四川</td>\n",
       "      <td>石渠</td>\n",
       "      <td>98.06</td>\n",
       "      <td>32.59</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>56045</td>\n",
       "      <td>青海</td>\n",
       "      <td>甘德</td>\n",
       "      <td>99.54</td>\n",
       "      <td>33.58</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>4050.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区站号  省份   站名     经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)    开始年份    结束年份  \\\n",
       "204  52908  青海  五道梁  93.05  35.13      4612.2        4613.0  198101  201012   \n",
       "879  55228  西藏  狮泉河  80.05  32.30      4278.6        4279.8  198101  201012   \n",
       "880  55248  西藏   改则  84.25  32.09      4414.9        4416.1  198101  201012   \n",
       "881  55299  西藏   那曲  92.04  31.29      4507.0        4508.2  198101  201012   \n",
       "883  55493  西藏   当雄  91.06  30.29      4200.0        4201.2  198101  201012   \n",
       "890  55664  西藏   定日  87.05  28.38      4300.0        4301.2  198101  201012   \n",
       "891  55680  西藏   江孜  89.36  28.55      4040.0        4041.2  198101  201012   \n",
       "892  56004  青海  沱沱河  92.26  34.13      4533.1        4533.9  198101  201012   \n",
       "893  56016  青海   治多  95.37  33.51      4179.1        4179.9  198101  201012   \n",
       "894  56018  青海   杂多  95.17  32.53      4066.4        4067.2  198101  201012   \n",
       "895  56021  青海  曲麻莱  95.48  34.07      4175.0        4175.8  198101  201012   \n",
       "897  56033  青海   玛多  98.13  34.55      4272.3        4273.1  198101  201012   \n",
       "898  56034  青海  清水河  97.08  33.48      4415.4        4416.2  198101  201012   \n",
       "899  56038  四川   石渠  98.06  32.59      4200.0        4201.0  198101  201012   \n",
       "901  56045  青海   甘德  99.54  33.58      4050.0        4050.8  198101  201012   \n",
       "\n",
       "    缺测时段        标准值统计时段  \n",
       "204  NaN  198101-201012  \n",
       "879  NaN  198101-201012  \n",
       "880  NaN  198101-201012  \n",
       "881  NaN  198101-201012  \n",
       "883  NaN  198101-201012  \n",
       "890  NaN  198101-201012  \n",
       "891  NaN  198101-201012  \n",
       "892  NaN  198101-201012  \n",
       "893  NaN  198101-201012  \n",
       "894  NaN  198101-201012  \n",
       "895  NaN  198101-201012  \n",
       "897  NaN  198101-201012  \n",
       "898  NaN  198101-201012  \n",
       "899  NaN  198101-201012  \n",
       "901  NaN  198101-201012  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观测场拔海高度大于4000的数据\n",
    "wData.loc[wData['观测场拔海高度（米）']>4000,:] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 复杂查询，组合条件用&符号合并,每个判断条件都得带括号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>省份</th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "      <th>开始年份</th>\n",
       "      <th>结束年份</th>\n",
       "      <th>缺测时段</th>\n",
       "      <th>标准值统计时段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>55664</td>\n",
       "      <td>西藏</td>\n",
       "      <td>定日</td>\n",
       "      <td>87.05</td>\n",
       "      <td>28.38</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>4301.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>55680</td>\n",
       "      <td>西藏</td>\n",
       "      <td>江孜</td>\n",
       "      <td>89.36</td>\n",
       "      <td>28.55</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>4041.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区站号  省份  站名     经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)    开始年份    结束年份  \\\n",
       "890  55664  西藏  定日  87.05  28.38      4300.0        4301.2  198101  201012   \n",
       "891  55680  西藏  江孜  89.36  28.55      4040.0        4041.2  198101  201012   \n",
       "\n",
       "    缺测时段        标准值统计时段  \n",
       "890  NaN  198101-201012  \n",
       "891  NaN  198101-201012  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wData.loc[(wData['纬度']<30) & (wData['观测场拔海高度（米）']>4000),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 调用函数查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>省份</th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "      <th>开始年份</th>\n",
       "      <th>结束年份</th>\n",
       "      <th>缺测时段</th>\n",
       "      <th>标准值统计时段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>52908</td>\n",
       "      <td>青海</td>\n",
       "      <td>五道梁</td>\n",
       "      <td>93.05</td>\n",
       "      <td>35.13</td>\n",
       "      <td>4612.2</td>\n",
       "      <td>4613.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>55228</td>\n",
       "      <td>西藏</td>\n",
       "      <td>狮泉河</td>\n",
       "      <td>80.05</td>\n",
       "      <td>32.30</td>\n",
       "      <td>4278.6</td>\n",
       "      <td>4279.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>55248</td>\n",
       "      <td>西藏</td>\n",
       "      <td>改则</td>\n",
       "      <td>84.25</td>\n",
       "      <td>32.09</td>\n",
       "      <td>4414.9</td>\n",
       "      <td>4416.1</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>55299</td>\n",
       "      <td>西藏</td>\n",
       "      <td>那曲</td>\n",
       "      <td>92.04</td>\n",
       "      <td>31.29</td>\n",
       "      <td>4507.0</td>\n",
       "      <td>4508.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>55493</td>\n",
       "      <td>西藏</td>\n",
       "      <td>当雄</td>\n",
       "      <td>91.06</td>\n",
       "      <td>30.29</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4201.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>56004</td>\n",
       "      <td>青海</td>\n",
       "      <td>沱沱河</td>\n",
       "      <td>92.26</td>\n",
       "      <td>34.13</td>\n",
       "      <td>4533.1</td>\n",
       "      <td>4533.9</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>56016</td>\n",
       "      <td>青海</td>\n",
       "      <td>治多</td>\n",
       "      <td>95.37</td>\n",
       "      <td>33.51</td>\n",
       "      <td>4179.1</td>\n",
       "      <td>4179.9</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>56018</td>\n",
       "      <td>青海</td>\n",
       "      <td>杂多</td>\n",
       "      <td>95.17</td>\n",
       "      <td>32.53</td>\n",
       "      <td>4066.4</td>\n",
       "      <td>4067.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>56021</td>\n",
       "      <td>青海</td>\n",
       "      <td>曲麻莱</td>\n",
       "      <td>95.48</td>\n",
       "      <td>34.07</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>4175.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>56033</td>\n",
       "      <td>青海</td>\n",
       "      <td>玛多</td>\n",
       "      <td>98.13</td>\n",
       "      <td>34.55</td>\n",
       "      <td>4272.3</td>\n",
       "      <td>4273.1</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>56034</td>\n",
       "      <td>青海</td>\n",
       "      <td>清水河</td>\n",
       "      <td>97.08</td>\n",
       "      <td>33.48</td>\n",
       "      <td>4415.4</td>\n",
       "      <td>4416.2</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>56038</td>\n",
       "      <td>四川</td>\n",
       "      <td>石渠</td>\n",
       "      <td>98.06</td>\n",
       "      <td>32.59</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>56045</td>\n",
       "      <td>青海</td>\n",
       "      <td>甘德</td>\n",
       "      <td>99.54</td>\n",
       "      <td>33.58</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>4050.8</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区站号  省份   站名     经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)    开始年份    结束年份  \\\n",
       "204  52908  青海  五道梁  93.05  35.13      4612.2        4613.0  198101  201012   \n",
       "879  55228  西藏  狮泉河  80.05  32.30      4278.6        4279.8  198101  201012   \n",
       "880  55248  西藏   改则  84.25  32.09      4414.9        4416.1  198101  201012   \n",
       "881  55299  西藏   那曲  92.04  31.29      4507.0        4508.2  198101  201012   \n",
       "883  55493  西藏   当雄  91.06  30.29      4200.0        4201.2  198101  201012   \n",
       "892  56004  青海  沱沱河  92.26  34.13      4533.1        4533.9  198101  201012   \n",
       "893  56016  青海   治多  95.37  33.51      4179.1        4179.9  198101  201012   \n",
       "894  56018  青海   杂多  95.17  32.53      4066.4        4067.2  198101  201012   \n",
       "895  56021  青海  曲麻莱  95.48  34.07      4175.0        4175.8  198101  201012   \n",
       "897  56033  青海   玛多  98.13  34.55      4272.3        4273.1  198101  201012   \n",
       "898  56034  青海  清水河  97.08  33.48      4415.4        4416.2  198101  201012   \n",
       "899  56038  四川   石渠  98.06  32.59      4200.0        4201.0  198101  201012   \n",
       "901  56045  青海   甘德  99.54  33.58      4050.0        4050.8  198101  201012   \n",
       "\n",
       "    缺测时段        标准值统计时段  \n",
       "204  NaN  198101-201012  \n",
       "879  NaN  198101-201012  \n",
       "880  NaN  198101-201012  \n",
       "881  NaN  198101-201012  \n",
       "883  NaN  198101-201012  \n",
       "892  NaN  198101-201012  \n",
       "893  NaN  198101-201012  \n",
       "894  NaN  198101-201012  \n",
       "895  NaN  198101-201012  \n",
       "897  NaN  198101-201012  \n",
       "898  NaN  198101-201012  \n",
       "899  NaN  198101-201012  \n",
       "901  NaN  198101-201012  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接写lambda表达式\n",
    "wData.loc[lambda wData: (wData['纬度']>30) & (wData['观测场拔海高度（米）']>4000),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区站号</th>\n",
       "      <th>省份</th>\n",
       "      <th>站名</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>观测场拔海高度（米）</th>\n",
       "      <th>气压传感器拔海高度(米)</th>\n",
       "      <th>开始年份</th>\n",
       "      <th>结束年份</th>\n",
       "      <th>缺测时段</th>\n",
       "      <th>标准值统计时段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>56033</td>\n",
       "      <td>青海</td>\n",
       "      <td>玛多</td>\n",
       "      <td>98.13</td>\n",
       "      <td>34.55</td>\n",
       "      <td>4272.3</td>\n",
       "      <td>4273.1</td>\n",
       "      <td>198101</td>\n",
       "      <td>201012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198101-201012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       区站号  省份  站名     经度     纬度  观测场拔海高度（米）  气压传感器拔海高度(米)    开始年份    结束年份  \\\n",
       "897  56033  青海  玛多  98.13  34.55      4272.3        4273.1  198101  201012   \n",
       "\n",
       "    缺测时段        标准值统计时段  \n",
       "897  NaN  198101-201012  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 编写自己的函数\n",
    "def query_data(data):\n",
    "    return data['区站号'] == 56033\n",
    "wData.loc[query_data,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas 数据新增\n",
    "直接赋值、apply、assign、分条件赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas怎样新增数据列\n",
    "在进行数据分析时，经常需要按照一定的条件创建新的数据列，然后进行近一步分析\n",
    "* 1、直接赋值\n",
    "* 2、df.apply方法\n",
    "* 3、df.assign方法\n",
    "* 4、按条件选择分组分别赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "tPath = './datas/tianqi.csv'\n",
    "tData = pd.read_csv(tPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3℃</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2℃</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2℃</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0℃</td>\n",
       "      <td>-8℃</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3℃</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd bWendu yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel\n",
       "0  2018-01-01     3℃    -6℃   晴~多云       东北风   1-2级   59       良         2\n",
       "1  2018-01-02     2℃    -5℃   阴~多云       东北风   1-2级   49       优         1\n",
       "2  2018-01-03     2℃    -5℃     多云        北风   1-2级   28       优         1\n",
       "3  2018-01-04     0℃    -8℃      阴       东北风   1-2级   28       优         1\n",
       "4  2018-01-05     3℃    -6℃   多云~晴       西北风   1-2级   50       优         1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直接赋值的方法\n",
    "实例：清理温度列，变成数字类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tData.loc[:,'bWendu'] = tData['bWendu'].str.replace('℃','').astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8℃</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd  bWendu yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel\n",
       "0  2018-01-01       3    -6℃   晴~多云       东北风   1-2级   59       良         2\n",
       "1  2018-01-02       2    -5℃   阴~多云       东北风   1-2级   49       优         1\n",
       "2  2018-01-03       2    -5℃     多云        北风   1-2级   28       优         1\n",
       "3  2018-01-04       0    -8℃      阴       东北风   1-2级   28       优         1\n",
       "4  2018-01-05       3    -6℃   多云~晴       西北风   1-2级   50       优         1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "tData.loc[:,'yWendu'] = tData['yWendu'].str.replace('℃','').astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd  bWendu  yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel\n",
       "0  2018-01-01       3      -6   晴~多云       东北风   1-2级   59       良         2\n",
       "1  2018-01-02       2      -5   阴~多云       东北风   1-2级   49       优         1\n",
       "2  2018-01-03       2      -5     多云        北风   1-2级   28       优         1\n",
       "3  2018-01-04       0      -8      阴       东北风   1-2级   28       优         1\n",
       "4  2018-01-05       3      -6   多云~晴       西北风   1-2级   50       优         1"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例：计算温差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意：tData['bWendu']其实是一个Series，后面的减法返回的是Series\n",
    "tData.loc[:,'wencha'] = tData['bWendu']-tData['yWendu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "      <th>wencha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd  bWendu  yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel  \\\n",
       "0  2018-01-01       3      -6   晴~多云       东北风   1-2级   59       良         2   \n",
       "1  2018-01-02       2      -5   阴~多云       东北风   1-2级   49       优         1   \n",
       "2  2018-01-03       2      -5     多云        北风   1-2级   28       优         1   \n",
       "3  2018-01-04       0      -8      阴       东北风   1-2级   28       优         1   \n",
       "4  2018-01-05       3      -6   多云~晴       西北风   1-2级   50       优         1   \n",
       "\n",
       "   wencha  \n",
       "0       9  \n",
       "1       7  \n",
       "2       7  \n",
       "3       8  \n",
       "4       9  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df.apply方法\n",
    "apply a function along an axis of DataFrame\n",
    "Objects passed to the function are Series objects whose index either the DataFrame's index(axis = 0) or the DataFrame's columns(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例：添加一列温度类型\n",
    "* 1、如果最高温度大于33度就是高温\n",
    "* 2、低于-10就是低温\n",
    "* 3、否则就是常温"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wendu_type(x):\n",
    "    if x['bWendu']>33:\n",
    "        return '高温'\n",
    "    if x['yWendu']<-10:\n",
    "        return '低温'\n",
    "    return '常温'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意：需要设置axis=1,这是series的index是columns\n",
    "tData.loc[:,'wendu_type'] = tData.apply(get_wendu_type,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "      <th>wencha</th>\n",
       "      <th>wendu_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>常温</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd  bWendu  yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel  \\\n",
       "0  2018-01-01       3      -6   晴~多云       东北风   1-2级   59       良         2   \n",
       "1  2018-01-02       2      -5   阴~多云       东北风   1-2级   49       优         1   \n",
       "2  2018-01-03       2      -5     多云        北风   1-2级   28       优         1   \n",
       "3  2018-01-04       0      -8      阴       东北风   1-2级   28       优         1   \n",
       "4  2018-01-05       3      -6   多云~晴       西北风   1-2级   50       优         1   \n",
       "\n",
       "   wencha wendu_type  \n",
       "0       9         常温  \n",
       "1       7         常温  \n",
       "2       7         常温  \n",
       "3       8         常温  \n",
       "4       9         常温  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "常温    328\n",
       "高温     29\n",
       "低温      8\n",
       "Name: wendu_type, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看温度类型的计数\n",
    "tData['wendu_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df.assign方法\n",
    "Assign new columns to DataFrame\n",
    "Return a new object with all original columns in addition to new ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例：将温度从摄氏度变成华氏度 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "      <th>wencha</th>\n",
       "      <th>wendu_type</th>\n",
       "      <th>wencha_type</th>\n",
       "      <th>yWendu_huashi</th>\n",
       "      <th>bWendu_huashi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>21.2</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>17.6</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>21.2</td>\n",
       "      <td>37.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>-5</td>\n",
       "      <td>-12</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>3级</td>\n",
       "      <td>48</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>低温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>10.4</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-3</td>\n",
       "      <td>-11</td>\n",
       "      <td>晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>3级</td>\n",
       "      <td>40</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>低温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>12.2</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>-3</td>\n",
       "      <td>-12</td>\n",
       "      <td>晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>2级</td>\n",
       "      <td>29</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>低温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>10.4</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>-2</td>\n",
       "      <td>-11</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1级</td>\n",
       "      <td>31</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>低温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>12.2</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-2</td>\n",
       "      <td>-10</td>\n",
       "      <td>多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1级</td>\n",
       "      <td>56</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ymd  bWendu  yWendu tianqi fengxiang fengli  aqi aqiInfo  \\\n",
       "0    2018-01-01       3      -6   晴~多云       东北风   1-2级   59       良   \n",
       "1    2018-01-02       2      -5   阴~多云       东北风   1-2级   49       优   \n",
       "2    2018-01-03       2      -5     多云        北风   1-2级   28       优   \n",
       "3    2018-01-04       0      -8      阴       东北风   1-2级   28       优   \n",
       "4    2018-01-05       3      -6   多云~晴       西北风   1-2级   50       优   \n",
       "..          ...     ...     ...    ...       ...    ...  ...     ...   \n",
       "360  2018-12-27      -5     -12   多云~晴       西北风     3级   48       优   \n",
       "361  2018-12-28      -3     -11      晴       西北风     3级   40       优   \n",
       "362  2018-12-29      -3     -12      晴       西北风     2级   29       优   \n",
       "363  2018-12-30      -2     -11   晴~多云       东北风     1级   31       优   \n",
       "364  2018-12-31      -2     -10     多云       东北风     1级   56       良   \n",
       "\n",
       "     aqiLevel  wencha wendu_type wencha_type  yWendu_huashi  bWendu_huashi  \n",
       "0           2       9         常温         温差小           21.2           37.4  \n",
       "1           1       7         常温         温差小           23.0           35.6  \n",
       "2           1       7         常温         温差小           23.0           35.6  \n",
       "3           1       8         常温         温差小           17.6           32.0  \n",
       "4           1       9         常温         温差小           21.2           37.4  \n",
       "..        ...     ...        ...         ...            ...            ...  \n",
       "360         1       7         低温         温差小           10.4           23.0  \n",
       "361         1       8         低温         温差小           12.2           26.6  \n",
       "362         1       9         低温         温差小           10.4           26.6  \n",
       "363         1       9         低温         温差小           12.2           28.4  \n",
       "364         2       8         常温         温差小           14.0           28.4  \n",
       "\n",
       "[365 rows x 14 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以同时添加多个新的列,返回一个新的DataFrame\n",
    "tData.assign(\n",
    "    # 摄氏度转换为华氏度\n",
    "    yWendu_huashi = lambda x: x['yWendu'] * 9/5 +32,\n",
    "    bWendu_huashi = lambda x: x['bWendu'] * 9/5 +32\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按条件选择分组分别赋值\n",
    "按条件先选择数据，然后对这部分数据赋值新列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例：高低温差大于10度，则认为温差大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先创建空列（第一种创建新列的方法）\n",
    "tData['wencha_type'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "tData.loc[tData['bWendu']-tData['yWendu']>10,'wencha_type'] = '温差大'\n",
    "tData.loc[tData['bWendu']-tData['yWendu']<=10,'wencha_type'] = '温差小'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "温差小    187\n",
       "温差大    178\n",
       "Name: wencha_type, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData['wencha_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ymd</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "      <th>wencha</th>\n",
       "      <th>wendu_type</th>\n",
       "      <th>wencha_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>晴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>59</td>\n",
       "      <td>良</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>阴~多云</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>49</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>2</td>\n",
       "      <td>-5</td>\n",
       "      <td>多云</td>\n",
       "      <td>北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>阴</td>\n",
       "      <td>东北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>28</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "      <td>多云~晴</td>\n",
       "      <td>西北风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>50</td>\n",
       "      <td>优</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>常温</td>\n",
       "      <td>温差小</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ymd  bWendu  yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel  \\\n",
       "0  2018-01-01       3      -6   晴~多云       东北风   1-2级   59       良         2   \n",
       "1  2018-01-02       2      -5   阴~多云       东北风   1-2级   49       优         1   \n",
       "2  2018-01-03       2      -5     多云        北风   1-2级   28       优         1   \n",
       "3  2018-01-04       0      -8      阴       东北风   1-2级   28       优         1   \n",
       "4  2018-01-05       3      -6   多云~晴       西北风   1-2级   50       优         1   \n",
       "\n",
       "   wencha wendu_type wencha_type  \n",
       "0       9         常温         温差小  \n",
       "1       7         常温         温差小  \n",
       "2       7         常温         温差小  \n",
       "3       8         常温         温差小  \n",
       "4       9         常温         温差小  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tData.head()"
   ]
  }
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